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Abstract:We introduce a tool to build a surface mesh able to deal with sparse, heterogeneous, non-parallel, cross-sectional, non-coincidental contours and show its application to reconstruct surfaces of the heart. In recent years, much research has looked at creating personalised 3D anatomical models of the heart. These models usually incorporate a geometrical reconstruction of the anatomy in order to better understand cardiovascular functions as well as predict different cardiac processes. As MRIs are becoming the standard for cardiac medical imaging, we tested our methodology on cardiac MRI data from standard acquisitions. However, the ability to accurately reconstruct heart anatomy in three dimensions commonly comes with fundamental challenges-notably, the trade-off between data fitting and expected visual appearance. Most current techniques can either require contours from parallel slices or, if multiple slice orientations are used, require an exact match between these contours. In addition, some methods introduce a bias by the use of prior shape models or by trade-offs between the data matching terms and the smoothing terms. Our approach uses a composition of smooth approximations towards the maximization of the data fitting, ensuring a good matching to the input data as well as pleasant interpolation characteristics. To assess our method in the task of cardiac mesh generations, we evaluated its performance on synthetic data obtained from a cardiac statistical shape model as well as on real data. Using a statistical shape model, we simulated standard cardiac MRI acquisitions planes and contour data. We performed a multi-parameter evaluation study using plausible cardiac shapes generated from the model. We also show that long axes contours as well as the most extremal slices (basal and apical) contain the most amount of structural information, and thus should be taken into account when generating anatomically relevant geometrical cardiovascular surfaces. Our method is both used on epicardial and endocardial left ventricle surfaces as well as on the right ventricle.

Cardiac Magnetic Resonance (CMR) provides unique functional and anatomical visualisation of the macro and micro-structures of the heart. However, CMR acquisition times usually necessitate slices to be acquired at different breath holds, which results in potential misalignment of the acquired slices. Correcting for this spatial misalignment is required for accurate three-dimensional (3D) reconstruction of the heart chambers allowing robust metrics for shape analysis among populations as well as precise representations of individual geometries and scars. While several methods have been proposed to realign slices, their use in other important protocols such as late gadolinium enhancement ( LGE) is yet to be demonstrated. We propose a registration framework based on local phase to correct for slice misalignment. Our registration framework is a group registration technique combining long-and short-axis slices. Validation was performed on LGE slices using expert-traced ventricular contours. For 15 clinical multi-breath-hold datasets our method reduced the median discrepancy of moderately misaligned slices from 2.19 mm to 1.63 mm, and of severely misaligned from 7.33 mm to 1.96 mm.

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